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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Informatika Komputer UG Journal International Journal of Advances in Intelligent Informatics Jurnas Nasional Teknologi dan Sistem Informasi CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah KOMPUTASI Journal of Social Research Postulat : Jurnal Inovasi Pendidikan Matematika International Journal of Scientific and Professional ICONESTH Jurnal Pengabdian Kepada Masyarakat Darma Saskara
Dina Indarti
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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Journal : CESS (Journal of Computer Engineering, System and Science)

 1 
Rancang Bangun Dan Pengujian Kualitas Aplikasi Web E-Recruitment Berdasarkan ISO/IEC 9126 Pada Suatu Perusahaan Pembiayaan Di Indonesia Dedi Iskandar; Dina Indarti
CESS (Journal of Computer Engineering, System and Science) Vol 6, No 1 (2021): Januari 2021
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1009.756 KB) | DOI: 10.24114/cess.v6i1.19773

Abstract

This research aims to design, create, and test the quality of e-recruitment web applications at a financing company in Indonesia based on ISO/IEC 9126. Methods used in this study are Research and Development (R&D) including potential identification and problems, analysis and data collection, application design, implementation, application validation, application revision, and application trials. E-Recruitment web application was created using LAMP (Linux, Apache, MySQL, PHP). Application quality testing based on 6 factors namely functionality, reliability, efficiency, usability, maintainability, and portability. Functionality factor was tested using black box testing. The efficiency factor was tested using GT Metrix (Yslow) and PageSpeed Insight. The reliability factor was tested using stress testing (LoadStrom, LoadImpact, and WAPT 8.1). The maintainability factor was tested based on instrumentation, consistency, and simplicity aspects. The portability factor was tested using multiple web browsers, and usability factor using Computer System Usability Questionnaire. Based on the results of the research that has been done, e-recruitment web application is able to run well with some revisions from recruitment experts and programming experts. Application test results based on ISO/IEC 9126 have met the application quality factor standards of functionality, reliability, usability, maintainability, and portability. The efficiency factor still do not meet the application quality factor standards.
Penerapan Algoritma Support Vector Machine Dalam Pengenalan Wajah Berdasarkan Fitur Isomap Kosasih, Rifki; Mardhiyah, Iffatul; Indarti, Dina
CESS (Journal of Computer Engineering, System and Science) Vol. 11 No. 1 (2026): Januari 2026
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v11i1.68568

Abstract

Pengenalan wajah merupakan salah satu bidang yang digunakan untuk mengenali seseorang melalui citra ataupun video. Pengenalan wajah ini dapat digunakan untuk absensi kehadiran yang lebih efektif dan efisien dibandingkan dengan absensi menggunakan cara manual. Pada penelitian ini data yang digunakan merupakan data citra wajah yang terdiri dari 6 orang dengan tiap orang memiliki 4 variasi ekspresi wajah. Tahapan selanjutnya adalah melakukan ekstraksi fitur wajah dengan menggunakan metode isomap. Metode isomap adalah salah satu metode yang dapat mereduksi dimensi dari dimensi yang tinggi ke dimensi yang lebih rendah. Dalam studi ini dimensi yang dihasilkan sebanyak 4 sehingga terdapat 4 fitur yang akan digunakan dalam pengklasifikasian wajah. Fitur-fitur tersebut dibagi menjadi fitur latih dan fitur uji. Untuk pengklasifikasian wajah, digunakan metode support vector machine (SVM). Metode support vector machine merupakan metode supervised learning yang dapat digunakan dalam pengenalan pola dan klasifikasi. Metode support vector machine memperhatikan perhitungan jarak kedekatan fitur satu dengan fitur lainnya dalam pengenalan pola dan klasifikasi. Berdasarkan hasil klasifikasi diperoleh tingkat akurasi sebesar 87,5%, rata-rata terbobot presisi sebesar 79,1675% dan rata-rata terbobot recall sebesar 87,5%.   
Co-Authors Afifah, Kusniatul Aini Suri Talita Arvianda Iswandari Bertalya Bertalya Cahyaningtyas, Rizqia Dedi Iskandar Desti Riminarsih Fatimatul Khikmiyah Fazriyati, Elyna Gozzal, Reynaldo Mohammad Hutapea, Octaviani Ias Sri Wahyuni Ihsan Jatnika Ilmiyati Sari Ilmiyati Sari Karlina, Oktaviani Ella Lydia Mayasari Mardhiyah, Iffatul Nugraha, Mara Rifki Kosasih Rizki Mufrizal Sarifuddin Madenda Sri Syamsiah Yunike, Yunike